Every month, hundreds of studies are published in medical journals. Some are practice-changing, while many offer little value other than a credit in the author's bio. Sifting through the barrage of trials, guidelines, analyses, and reviews to find meaningful content can be difficult. It's more important now than ever that providers understand the principles of quality medical research and sound statistical techniques so they can decipher which information will improve their outcomes. After completing this program, providers will be able to:
- Define the four main types of medical studies, including their strengths and weaknesses
- Identify confounding and different types of bias
- Explain basic statistical concepts, including the normal distribution, confidence intervals, p-values, and study power
- Define different types of outcome analysis and recognize the problems with each
- Interpret and apply study measures, including relative and absolute risk, sensitivity, and specificity
Doctors order a lot of tests, many of which have a sensitivity and specificity for some condition. On the surface, these terms seem self-explanatory. If a test is sensitive, it's usually positive if a condition is present. If a test is specific, a positive test makes the condition very likely. Most people understand these concepts, but applying the information can sometimes be confusing. To illustrate, I'm going to present you with a clinical scenario.
A very distraught 52-year-old female comes to see you in your clinic. Your colleague ordered a Cologuard test on her, and it came back positive. Someone from your office called her and gave her the results, and since then, she has been unable to sleep and having daily panic attacks because she is afraid she is going to die from colon cancer. She tells you she went to the Cologuard website and read that the test has a sensitivity of 92% and a specificity of 90%. She starts to cry hysterically and says, "That means there is only a 10% chance the test is wrong."
So what do you want to tell her? Does she really have a 90% chance of having colon cancer? Let's break these two measures down and see if we can give her an educated answer.
First, let's tackle sensitivity, defined as the percentage of people with the disease who get a positive result. If a test has high sensitivity, almost everyone with the disease will have a positive test. Cologuard has a sensitivity of 92%, which means only 8% of people with colon cancer will get a negative result (false negatives). High sensitivity tests are most useful when negative because it means the disease is very unlikely. The significance of a positive result depends on the specificity.
Specificity is the percentage of people without the disease who get a negative result. Cologuard has a specificity of 90% which means only 10% of people without colon cancer will get a positive result (false positives). A test with high specificity is most useful when positive because it makes the disease likely. The illustrations below help to explain the relationship. [71]
The AAFP has reviewed this program and deemed it acceptable for AAFP credit.
Providers should claim only the credit commensurate with the extent of their
participation in the activity.
AMA/ AAFP Prescribed credit is accepted by the American Medical Association as
equivalent to AMA PRA Category 1 credit(s)™ toward the AMA Physician's Recognition
Award. When applying for the AMA PRA, Prescribed credit earned must be reported as
Prescribed, not as
